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In this semester project we develop an automated system to rank panoramas captured by the EPFL Livecam according to how visually appealing they are. In other words, a system able to predict an average of the rating of human users for such panoramas. As a w ...
A key aspect of constructing highly scalable Deep-learning microelectronic systems is to implement fault tolerance in the learning sequence. Error-injection analyses for memory is performed using a custom hardware model implementing parallelized restricted ...
Deep learning presents notorious computational challenges. These challenges in- clude, but are not limited to, the non-convexity of learning objectives and estimat- ing the quantities needed for optimization algorithms, such as gradients. While we do not a ...
Restricted Boltzmann Machines (RBMs) are widely used as building blocks for deep learning models. Learning typically proceeds by using stochastic gradient descent, and the gradients are estimated with sampling methods. However, the gradient estimation is a ...
Automatic face analysis has to cope with pose and lighting variations. Especially pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization procedures. We propose a data-driven face analysis appro ...
Automatic face analysis has to cope with pose and lighting variations. Especially pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization procedures. We propose a data-driven face analysis appro ...
We study the Darcy problem with log-normal permeability, modeling the fluid flow in a heterogeneous porous medium. A perturbation approach is adopted, expanding the solution in Taylor series around the nominal value of the permeability. The resulting recur ...
We present a description of transition rate matrices of models for Stochastic Automata Networks (SAN) in their representation as operators in Tensor Train (TT) format. The collection contains models from real-life applications both taken from references di ...
Automatic face analysis has to cope with pose and lighting variations. Especially pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization procedures. We propose a data-driven face analysis appro ...
In this paper we discuss a neural networks-based face analysis approach that is able to cope with faces subject to pose and lighting variations. Especially head pose variations are difficult to tackle and many face analysis methods require the use of sophi ...
